| dc.contributor.author | Geenens, Gery | |
| dc.contributor.author | Nieto Reyes, Alicia | |
| dc.contributor.author | Francisci, Giacomo | |
| dc.contributor.other | Universidad de Cantabria | es_ES |
| dc.date.accessioned | 2024-03-18T17:14:02Z | |
| dc.date.available | 2024-03-18T17:14:02Z | |
| dc.date.issued | 2023-04 | |
| dc.identifier.issn | 0960-3174 | |
| dc.identifier.issn | 1573-1375 | |
| dc.identifier.other | MTM2017-86061-C2-2-P | es_ES |
| dc.identifier.uri | https://hdl.handle.net/10902/32315 | |
| dc.description.abstract | The concept of depth has proved very important for multivariate and functional data analysis, as it essentially acts as a surrogate for the notion of ranking of observations which is absent in more than one dimension. Motivated by the rapid development of technology, in particular the advent of "Big Data", we extend here that concept to general metric spaces, propose a natural depth measure and explore its properties as a statistical depth function. Working in a general metric space allows the depth to be tailored to the data at hand and to the ultimate goal of the analysis, a very desirable property given the polymorphic nature of modern data sets. This flexibility is thoroughly illustrated by several real data analyses | es_ES |
| dc.description.sponsorship | Gery Geenens’ research was supported by a Faculty Research Grant from the Faculty of Science, UNSW Sydney, Australia. Alicia Nieto-Reyes’ research was funded by the Spanish Ministerio de Ciencia, Innovación y Universidades Grant Number MTM2017-86061-C2-2-P. | es_ES |
| dc.format.extent | 15 p. | es_ES |
| dc.language.iso | eng | es_ES |
| dc.publisher | Springer | es_ES |
| dc.rights | © The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. | es_ES |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
| dc.source | Statistics and Computing, 2023, 33(2), 46 | es_ES |
| dc.subject.other | Functional Data Analysis | es_ES |
| dc.subject.other | Lens depth | es_ES |
| dc.subject.other | Metric space | es_ES |
| dc.subject.other | Statistical depth | es_ES |
| dc.subject.other | Symbolic data análisis | es_ES |
| dc.title | Statistical depth in abstract metric spaces | es_ES |
| dc.type | info:eu-repo/semantics/article | es_ES |
| dc.relation.publisherVersion | https://doi.org/10.1007/s11222-023-10216-4 | es_ES |
| dc.rights.accessRights | openAccess | es_ES |
| dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2017-86061-C2-2-P/ES/REMUESTREO, RECORTES Y METRICAS PROBABILISTICAS. DATOS FUNCIONALES, PROYECCIONES ALEATORIAS Y PROFUNDIDADES ESTADISTICAS. APLICACIONES/ | |
| dc.identifier.DOI | 10.1007/s11222-023-10216-4 | |
| dc.type.version | publishedVersion | es_ES |
Excepto si se señala otra cosa, la licencia del ítem se describe como © The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
